Blockchain as a Service: A Decentralized and Secure Computing Paradigm

TL;DR

This paper proposes a decentralized, secure, and privacy-preserving computing paradigm using blockchain, decentralized learning, homomorphic encryption, and SDN to enable cooperative data analysis across untrustworthy and scattered nodes.

Contribution

It introduces a novel blockchain-based paradigm that addresses security, privacy, and decentralization challenges in distributed data analytics environments.

Findings

Effective in enabling cooperative computing among untrustworthy nodes

Enhances security and privacy in distributed data analysis

Demonstrates promising performance in simulation scenarios

Abstract

Thanks to the advances in machine learning, data-driven analysis tools have become valuable solutions for various applications. However, there still remain essential challenges to develop effective data-driven methods because of the need to acquire a large amount of data and to have sufficient computing power to handle the data. In many instances these challenges are addressed by relying on a dominant cloud computing vendor, but, although commercial cloud vendors provide valuable platforms for data analytics, they can suffer from a lack of transparency, security, and privacy-perservation. Furthermore, reliance on cloud servers prevents applying big data analytics in environments where the computing power is scattered. To address these challenges, a decentralize, secure, and privacy-preserving computing paradigm is proposed to enable an asynchronized cooperative computing process amongst scattered and untrustworthy computing nodes that may have limited computing power and computing intelligence. This paradigm is designed by exploring blockchain, decentralized learning, homomorphic encryption, and software defined networking(SDN) techniques. The performance of the proposed paradigm is evaluated via different scenarios in the simulation section.